Predictive Analysis for Social Processes II: Predictability and Warning Analysis

This two-part paper presents a new approach to predictive analysis for social processes. Part I identifies a class of social processes, called positive externality processes, which are both important and difficult to predict, and introduces a multi-scale, stochastic hybrid system modeling framework for these systems. In Part II of the paper we develop a systems theory-based, computationally tractable approach to predictive analysis for these systems. Among other capabilities, this analytic methodology enables assessment of process predictability, identification of measurables which have predictive power, discovery of reliable early indicators for events of interest, and robust, scalable prediction. The potential of the proposed approach is illustrated through case studies involving online markets, social movements, and protest behavior

Challenges in Complex Systems Science

FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda

Evaluating the role of quantitative modeling in language evolution

Models are a flourishing and indispensable area of research in language evolution. Here we highlight critical issues in using and interpreting models, and suggest viable approaches. First, contrasting models can explain the same data and similar modelling techniques can lead to diverging conclusions. This should act as a reminder to use the extreme malleability of modelling parsimoniously when interpreting results. Second, quantitative techniques similar to those used in modelling language evolution have proven themselves inadequate in other disciplines. Cross-disciplinary fertilization is crucial to avoid mistakes which have previously occurred in other areas. Finally, experimental validation is necessary both to sharpen models' hypotheses, and to support their conclusions. Our belief is that models should be interpreted as quantitative demonstrations of logical possibilities, rather than as direct sources of evidence. Only an integration of theoretical principles, quantitative proofs and empirical validation can allow research in the evolution of language to progress

Herding with and without Payoff Externalities - An Internet Experiment

Most real world situations that are susceptible to herding are also characterized by direct payoff externalities. Yet, the bulk of the theoretical and experimental literature on herding has focused on pure informational externalities. In this paper we experi- mentally investigate the effects of several different forms of payoff externalities (e.g., network effects, first-mover advantage, etc.) in a standard information-based herding model. Our results are based on an internet experiment with more than 6000 subjects, including a subsample of 267 consultants from an international consulting firm. We also replicate and review earlier cascade experiments. Finally, we study reputation e¤ects (i.e., the influence of success models) in the context of herding.information cascades, herding, network e¤ects, experiment, internet.

The benefits of social influence in optimized cultural markets

Social influence has been shown to create significant unpredictability in cultural markets, providing one potential explanation why experts routinely fail at predicting commercial success of cultural products. As a result, social influence is often presented in a negative light. Here, we show the benefits of social influence for cultural markets. We present a policy that uses product quality, appeal, position bias and social influence to maximize expected profits in the market. Our computational experiments show that our profit-maximizing policy leverages social influence to produce significant performance benefits for the market, while our theoretical analysis proves that our policy outperforms in expectation any policy not displaying social signals. Our results contrast with earlier work which focused on showing the unpredictability and inequalities created by social influence. Not only do we show for the first time that, under our policy, dynamically showing consumers positive social signals increases the expected profit of the seller in cultural markets. We also show that, in reasonable settings, our profit-maximizing policy does not introduce significant unpredictability and identifies "blockbusters". Overall, these results shed new light on the nature of social influence and how it can be leveraged for the benefits of the market

Climate Variability and the Millennium Development Goal Hunger Target

Climate variability contributes significantly to poverty and food insecurity. Proactive approaches to managing climate variability within vulnerable rural communities and among institutions operating at community, sub-national, and national levels is a crucial step toward achieving the Millennium Development Goal of eradicating extreme poverty and hunger. Climate variability can impact a household's access to food by affecting subsistence production, income from primary production, local food prices, and sometimes the economy of an entire region. The risk of household food insecurity is determined by the success of livelihood strategies in the face of climate and other shocks. Across the economy, climate variability affects food security through its influence on investment, adoption of agricultural technology, aggregate production, market prices and economic development, and hence the ability of individuals, communities and nations to produce and purchase food. The impacts of climate variability are both ex post – losses that follow a climate shock — and ex ante — opportunity costs of conservative risk management responses to climatic uncertainty. The report summarizes the scientific basis, current methodology, and prospects for improving climate prediction at a seasonal time scale. Current forecast methods give modest to moderately-high prediction skill in "hunger hotspots" in East, West and Southern Africa, and other regions in the tropics and subtropics. Applications of climate information contribute to a comprehensive strategy to combat hunger. First is the use of seasonal climate prediction in early warning systems to guide interventions to avert food crises. Second is the use of climate information to manage risk in agricultural systems within vulnerable rural communities and among a range of institutions. This includes smallholder farmers who comprise the largest group of poor and food-insecure; intermediary institutions that interface with farmers, and can provide the information, technical guidance and production inputs required for effective climate risk management; and institutions that make climate-sensitive decisions at a broader scale that influence food security. We also discuss measures to strengthen institutional capacity and coordination to improve management of climate variability. Improved management of climate variability has appealing synergies with other interventions that target hunger, including soil fertility management, small-scale water management, markets, and extension and communication systems